In Brazil, couples that want to adopt children need to go through an extensive examination and backround check. After they are accepted into the adoption proccess, they also need to fill a form in which they declare which kind of child they are willing to take in. This is when characteristics such as age, existance of siblings, special needs, gender and race come into play.
With wide repercussion in Brazil, the article was quoted by Senator Kátia Abreu two days after publication – mentioning the newspaper’s name directly in her speech. She was announcing the creation of a local branch for the National Association of Adoption Support Groups, at the state that she represents.
In the social networks, internet users emphasized the sensitivity of the subject, as well as the importance of the topic.
The wannabe adoptive parents are looking for very specific traits. For most of the children and teenagers living at government foster homes, this makes the odds of moving in with a new family very low. Age is the most decisive factor: after one turns 10, the chances drop drastically. Along with that, having disabilities and siblings are also key factors.
To show how parental preferences impact the likelihood of adoption, we developed a simulator that displays how long it would take for specific children to be selected. Each individual child is represented as a plant that grows as time passes.
What was the hardest part of this project?
The team responsible for the report had to develop its own simulator with three different databases. The first one brings figures about the characteristics of the population of children and adolescents in Brazil and the second one, the National Registry of Adoption (CNA), which details what are the characteristics that the applicants seek when adopting.
The third database is the result of simulations made by Estadão. Based on real numbers from the two sources above, we created an algorithm that generates children and suitors – and then checks if there was a “match” between them. The match happens when there is a child with all the characteristics that the parents are looking for.
An example. According to the CNA system – in consultation made on August 10, 2019 – there were 42,546 applicants in Brazil. Among them, 15,694 accepted to adopt a child who had siblings. This indicates that approximately 37% of the applicants accept to adopt children with siblings.
When the simulator generates a new suitor, it makes a kind of crown or coin to decide if this suitor accepts children with siblings. However, unlike a normal coin, where there is a 50% chance of one thing or another happening, this coin has a 37% chance of falling on one side. If it falls on this side, this suitor accepts to adopt siblings. Now imagine that we have made 100 tosses of this coin. The number of times it fell on the face of the “accepts to adopt children with siblings” tends to be 37. This means that it should be close to 37, but it could be 40 or 35, for example.
What can others learn from this project?
In addition to the technical challenge, we had to think about how not to distance the readers from the report. Naturally numbers and descriptive statistics are cold, so we had to think about how to humanize the data. Our choice was to represent each child as a plant. If it has a “v”, it has siblings and a flower, it means it has some kind of deficiency.